109 results about "Expectation–maximization algorithm" patented technology

The invention discloses a multi-dimensional distribution offset adaptive threshold detection method and system, and the method comprises the steps: obtaining real-time data, extracting a multi-dimensional statistical feature, and obtaining a feature vector; based on historical normal data, using an expectation maximization algorithm to train a Gaussian mixture model, and determining parameters to obtain a normal distribution model; inputting the feature vector into the model, and calculating a probability value of the feature vector belonging to normal distribution as a first offset judgment index; based on the real-time data distribution of a plurality of detection objects in the same group, the distribution difference of any two objects is calculated by using a Wasserstein distance, and the similarity between the objects is obtained; and constructing a similarity network and calculating connectivity as a second offset judgment index. Setting a fixed-length sliding window, dynamically updating two indexes in the window, and obtaining a first self-adaptive threshold value and a second self-adaptive threshold value; and when any index is lower than a corresponding threshold value, determining distribution offset and giving an alarm, and updating model parameters in real time by using an incremental expectation maximization algorithm. According to the invention, accurate detection and intelligent analysis of data distribution offset are realized.

The invention provides a communication auxiliary sensing system and method based on an intelligent super surface (RIS). The communication auxiliary sensing system comprises a multi-antenna base station, a multi-user terminal and an active RIS. According to the system, an uplink pilot frequency and a data signal are jointly utilized, an expectation maximization (EM) algorithm is adopted to iteratively recover equivalent channel state information (CSI), and a Cramer-Rao bound (CRB) of sensing performance is deduced. Combined optimization of user precoding and RIS reflection coefficients is innovatively proposed under the uncertainty of CSI, so as to minimize CRB and guarantee user communication rate at the same time. An original problem is converted into a processable form by establishing a traversal MIMO spectrum efficiency compact lower bound, and efficient optimization is realized by combining a WMMSE method with a block coordinate descent (BCD) frame. According to the scheme, the sensing precision and the spectrum efficiency are remarkably improved, the method is suitable for an active RIS-assisted MU-MIMO network, and the technology can also be expanded to a traditional RIS-free system.

The invention belongs to the technical field of wireless communication, and particularly relates to a positioning method based on low-orbit satellite opportunity signal Doppler measurement. The method solves the Doppler measurement positioning problem based on low orbit (LEO) satellite opportunity signals under the condition that GNSS signals are limited or unavailable. A positioning process is modeled as a non-linear state space framework, a joint estimation scheme combining a cubic Kalman smoother and an expectation maximization algorithm is provided, efficient estimation of a terminal position and Doppler frequency shift is realized, and noise covariance is adaptively optimized at the same time. The method provided by the invention theoretically ensures the optimal performance in statistical significance. A simulation result shows that compared with a traditional nonlinear least square method, the scheme provided by the invention can remarkably improve the positioning precision, and the calculation overhead is kept reasonable and balanced.

The invention discloses a natural electric field-electric field joint detection and inversion method based on GMM prior, and relates to the technical field of tunnel engineering geological detection, and the method comprises the steps: obtaining the resistivity and natural potential observed in a to-be-inverted region; an equivalent body source of a natural potential is parameterized into a body source vector on a grid unit, weighted L1 sparse regular constraint is introduced into the body source vector, a GMM prior model is used as underground electrical structure prior shared across physics fields, and a multi-task joint inversion objective function of a shared underground electrical structure label is established; and performing inversion iteration by adopting maximum posteriori, updating parameters of the GMM prior model and the underground electrical structure label by adopting an expectation maximization algorithm until an end condition is met, and obtaining a joint inversion imaging result of the tunnel water-rich anomalous body. The advantages of a multi-source electrical method are fused, underground electrical structure priori is introduced, Bayesian uncertainty analysis is combined, and high-resolution and high-reliability recognition of the water-rich anomalous body in front of the tunnel is achieved.

The invention relates to the technical field of satellite navigation positioning, in particular to a Beidou robust Kalman filtering positioning method based on a non-Gaussian noise model. According to the method, non-Gaussian noise generated by shielding and a multipath effect in Beidou positioning is accurately represented by constructing a Gaussian mixture model and a student t distribution model, model parameters are updated online by an expectation maximization algorithm, and the problem that a traditional Gaussian hypothesis model cannot adapt to noise statistical characteristics is solved; the non-Gaussian noise model is fused with robust filtering, a self-adaptive robust weight matrix is constructed through a Huber cost function, an IGGIII scheme or noise probability density, an observation covariance matrix is dynamically adjusted, the influence of abnormal observation values is suppressed, the defects that classical robust filtering lacks noise modeling and weight adjustment fails under continuous strong interference are overcome, and the robustness of the robust filtering is improved. And meanwhile, the problem of complex particle filtering calculation is avoided.

The invention discloses an Internet of Things anomaly detection method and system based on quaternion state space diffusion enhancement, and belongs to the technical field of network security and artificial intelligence. The method comprises the following steps: mapping a flow time sequence feature into a quaternion tensor to maintain an internal coupling relationship of a multi-dimensional feature; a double-flow encoder is designed, a quaternion selective state space model is adopted to extract continuous fluid features, and a dynamic hypergraph neural network is adopted to model discrete protocol features; carrying out self-supervised pre-training on a resistance pseudo-anomaly sample by utilizing potential diffusion model generation, and optimizing characterization by combining quaternion cepstrum distance loss; the injected learnable prompt vector is optimized in the small sample fine tuning stage, and a category prototype is corrected by using a semi-supervised expectation maximization algorithm; and calculating a sample anomaly score based on an energy model to realize known attack classification and unknown anomaly judgment. According to the method, the generalization ability of the model under the small sample condition and the unknown threat detection ability are remarkably improved.

The invention discloses a hybrid antenna array direction of arrival estimation method based on noise marginalization SBL. The method comprises the following steps: initializing system parameters and a sampling grid set; establishing a hybrid antenna array receiving signal model, and initializing a hybrid beam forming matrix into a block diagonal matrix meeting constant modulus constraint; constructing a hierarchical probability model under a Bayesian framework, and introducing a noise precision parameter into signal prior; integral operation is carried out on the noise precision parameter, so that the signal posterior distribution is converted into student t distribution; an objective function is constructed, an expectation maximization algorithm is adopted to iteratively update a signal energy spectrum, and the process does not involve noise parameter estimation; an alternating iteration strategy is adopted, the signal energy is fixedly updated through inner circulation, and optimization is performed through a gradient descent method after outer circulation is fixed; after convergence, constructing a target function of off-grid estimation by reconstructing a covariance matrix; and searching the off-grid direction of the maximized objective function near a spectrum peak to obtain a final DOA estimation value.

The invention discloses a frequency domain adaptive clutter suppression method and system for a dual-polarization weather radar, and relates to the technical field of weather radar processing. According to the invention, pulse-level quality control and ground feature identification marking are carried out on radar original echoes; calculating power spectrums of the marking units, performing dynamic noise measurement and data quality grading, and screening out effective processing units; constructing a Gaussian mixture model of a power spectrum of an effective unit, and iteratively estimating model parameters through an expectation maximization algorithm to realize complete separation of a ground feature spectrum and a meteorological spectrum in a frequency domain; inverting single-channel parameters based on the separated meteorological spectrum; a cooperative spectrum separation strategy is adopted for horizontal and vertical polarization channels, and dual-polarization parameters such as differential reflectivity, correlation coefficients and differential phases are inverted based on the filtered dual-channel signals; according to the invention, high-precision separation of clutters and meteorological echoes is realized, the loss of meteorological signals is significantly reduced while ground features are effectively suppressed, and the accuracy and reliability of dual-polarization parameter inversion are improved.

The invention relates to the technical field of target tracking, and discloses an LSTM-KF hybrid tracking method based on parameter adaptation, which comprises the following steps: acquiring target position measurement data of a moving target, and extracting dual-channel characteristics of the target position measurement data through a sliding window; inputting the dual-channel features into a pre-trained dual-channel LSTM network to obtain a target position prediction result and a target speed prediction result; analyzing target motion characteristics based on a prediction result, selecting a corresponding dynamic model according to a motion mode, and performing target state estimation on the dynamic model through a Kalman filter; and finally, optimizing noise parameters of the Kalman filter by adopting an expectation maximization algorithm, carrying out weighted fusion on a prediction result of the dual-channel LSTM network and a target state estimation result, and outputting a final target state. According to the invention, high-precision and high-robustness target tracking in a complex environment is realized.

The invention discloses an intelligent flood forecasting method for coupling error correction and joint modeling, and relates to a deep learning and uncertainty modeling technology. At the input end, constructing a future random rainfall scene through hourly dynamic normal disturbance; the method comprises the following steps: at a model end, introducing a multi-structure and multi-objective function combination based on Kolmogorov-Arnold Networks and Transform, and forming a multi-member ensemble forecast; at an error end, a probabilistic modeling method based on a numerable asymmetric Laplacian mixed density network is provided, and fine error correction is realized; a Vine copula function is adopted to construct high-dimensional joint distribution, and a Bayesian model averaging and expectation maximization algorithm is combined to realize weighted fusion of multi-member posterior results; according to the method, uncertainty in flood forecasting can be comprehensively described, the stability and adaptability of a forecasting system are improved, and the method is suitable for a basin-level real-time flood ensemble forecasting scene.

The invention discloses a radar interference effect evaluation method based on a constraint learning dynamic Bayesian network, is applied to the field of radar interference evaluation, and aims at solving the problem that the accuracy of interference effect evaluation is reduced due to radar detection data missing in a complex electromagnetic environment. Meanwhile, parameter constraints of five types of evaluation indexes and interference effect grades are defined; secondly, constructing a constraint learning dynamic Bayesian network, and learning a conditional probability and a transition probability under a data missing condition; then, proposing a prior constraint expectation maximization algorithm, converting parameter learning into an optimization problem with constraint by combining convex optimization, and overcoming the defects of a traditional expectation maximization algorithm; secondly, a cloud model is introduced to quantify discrete probability distribution into a continuous interference degree value; finally, simulation shows that the method can effectively improve parameter learning stability and evaluation accuracy under the conditions of suppressing and deception jamming and single index deficiency, and provides a reliable scheme for radar jamming effect evaluation in a complex environment.

The invention discloses a multi-dimensional night urination behavior monitoring system based on rhythm features, and relates to the technical field of biomedical signal processing, and the system specifically comprises a data acquisition module, a rhythm feature extraction module, a urine volume dynamics modeling module, a clustering monitoring module and a real-time feedback module; collecting individual data in real time through IoT equipment and preprocessing the individual data; calculating a nocturnal urination frequency index, a nocturnal urination time concentration ratio, a urination interval rhythm variation coefficient and a deviation index based on the individual data, and performing rhythm feature extraction; performing quadratic polynomial least square fitting on the night accumulated urine volume, and calculating a urine volume acceleration index by using an obtained second derivative to quantify a urine volume generation trend; the method comprises the following steps: constructing and preprocessing a night urine multi-dimensional digital phenotypic vector matrix, fitting a Gaussian mixture model based on an expectation maximization algorithm of a Bayesian information criterion, automatically mapping individuals into four types of subtypes according to cluster center features, and outputting individual subtype labels and confidence coefficients; and obtaining a comprehensive risk score through normalized risk assessment.

The application provides a spectrum overlapping peak decomposition method, device, equipment and medium, the method comprises: to X ray fluorescence spectrum, through the global search of particle swarm optimization algorithm APU-PSO with adaptive parameter updating strategy, obtain the initial parameter of Gaussian mixture model GMM, the initial parameter includes the peak position, variance and the weight of each sub-peak in the overlapping peak Gaussian characteristic peak, and the GMM models the overlapping peak as the superposition of multiple Gaussian characteristic peaks; based on the initial parameter, the parameters of GMM are iteratively optimized through expectation maximization algorithm EM, the optimal parameters of each Gaussian characteristic peak are obtained, and then the optimal parameter set containing the peak position, variance and the weight of each sub-peak in the total peak area of each Gaussian characteristic peak is determined. The application can effectively improve the accuracy and reliability of X ray fluorescence spectrum overlapping peak decomposition.

The application discloses a lithium battery residual life prediction method based on a multi-stage Wiener process, relates to the technical field of lithium batteries, and establishes a lithium battery degradation model based on a Wiener process; a degradation state and model parameters are adaptively estimated based on an expectation maximization EM algorithm and a Kalman smoothing method; in order to predict the residual life at the current moment, the degradation state at a change point is considered to be unknown, a probability density function of the residual life is solved, and an estimated value of the residual life is obtained. The application adopts the above lithium battery residual life prediction method based on the multi-stage Wiener process, establishes a multi-stage Wiener process degradation model based on measurement error, solves and simplifies the probability density function of the residual life, and improves the accuracy of lithium battery residual life prediction.

The application provides a continuous casting crystallizer vibration device residual life prediction method, comprising the following steps: S1. obtaining the vibration deflection of the continuous casting crystallizer vibration device to be measured and taking the vibration deflection as degradation data; S2. constructing a continuous casting crystallizer vibration device degradation model; S3. determining a drift coefficient in the degradation model and a parameter vector of the drift coefficient based on a nonlinear function regression algorithm; S4. estimating the prior parameters of the continuous casting crystallizer vibration device degradation model based on an expectation maximization (EM) algorithm and determining optimal prior parameters θ; S5. constructing a residual life probability density model, substituting the prior parameters into the residual life probability density model to calculate residual life probability values, and taking the residual life corresponding to the maximum probability value as the final prediction value of the residual life of the continuous casting crystallizer vibration device; only the degradation data of the continuous casting crystallizer vibration device to be measured is needed to accurately predict the residual life of the vibration device.

The invention discloses a two-stage adaptive numerical distribution reconstruction method based on local differential privacy. And the terminal equipment applies a local differential privacy perturbation mechanism to the held numerical private data to generate perturbation data and sends the perturbation data to the aggregation server. After an aggregation server collects data, first-stage estimation is executed firstly, and initial smooth distribution with low variance characteristics is generated based on an EMS algorithm to serve as a guide map; then, a non-uniform self-adaptive bucket dividing strategy is constructed according to cumulative distribution characteristics of initial distribution, and equal probability interval division is achieved; and finally, second-stage estimation is executed, re-statistics is carried out on noise data based on a self-adaptive bucket dividing strategy, accurate reconstruction is carried out by applying an expectation maximization algorithm with space kernel smoothing, and weighted updating is carried out on threshold-controlled space smoothing by utilizing a kernel function based on a bucket center physical distance. According to the method, the problem of over-fitting of discretization deviation of uniform bucket division and an expectation maximization algorithm in a local differential privacy high-noise environment is effectively solved through a two-stage strategy, and the accuracy and robustness of value distribution estimation are remarkably improved.

The invention relates to the technical field of power systems, in particular to a wind-light and load typical scene optimization method. The method comprises the following steps: establishing a mathematical model of core equipment in the multi-energy coupling system; on the basis of the mathematical model, an improved Gaussian mixture model and an improved expectation maximization algorithm are adopted, and a typical scene set with probability distribution fitting reality is generated in combination with space-time correlation characteristics of wind and light output and loads; the method comprises the following steps of: constructing an optimal scheduling model containing multi-energy supply and demand balance constraints by taking system total cost minimization as a target and combining an operation boundary condition and a typical scene set of core equipment, and solving the optimal scheduling model after performing linearization processing on the optimal scheduling model to obtain a flexibility adjustment scheme of the multi-energy coupling system. According to the method, the influence of multiple uncertain factors on the system regulation capability can be comprehensively reflected, the modeling precision of the probability distribution of uncertain variables such as wind, light, load and the like is improved, and the accuracy of the flexibility evaluation of the power system and the rationality of resource allocation are improved.

This application relates to the field of computer vision technology and discloses an anomaly event detection method, apparatus, device, storage medium, and product. The method includes: based on a deep neural network encoder, mapping video event samples to a feature space to obtain video features and initial clusters; based on the expectation-maximization algorithm, obtaining optimized clusters, an updated deep neural network encoder, and a prior distribution based on the video features and initial clusters; and detecting anomaly events in the video based on the optimized clusters, the updated deep neural network encoder, and the prior distribution. This application, through the expectation-maximization algorithm, can iteratively optimize the initial clusters and deep neural network encoder based on video features and initial clusters, and learn the prior distribution, improving cluster accuracy and better adapting to data characteristics. This results in more accurate extraction of video features, better differentiation between normal and abnormal events during anomaly detection, and improved detection accuracy.

The present application relates to a method for optimizing energy consumption of an electric-thermal integrated energy system with reduced loss, comprising: obtaining energy consumption data of the electric-thermal integrated energy system; processing the obtained energy consumption data, classifying it according to energy consumption regions, and dividing time periods; analyzing the energy consumption data of different regions respectively, obtaining consumption sources; optimizing the energy consumption of different regions respectively, generating improvement suggestions for the consumption sources; implementing loss reduction measures, and analyzing and comparing the loss reduction effects, recording the effective loss reduction measures for popularization and use. The present application also discloses an energy consumption optimization system. The present application obtains energy consumption data of different regions and different time intervals, monitors energy consumption abnormal nodes in real time; uses a normal distribution probability model of an expectation maximization algorithm to calculate the optimal energy consumption data distribution, generates improvement suggestions for the consumption sources, and optimizes energy consumption.

The invention discloses a low signal-to-noise ratio ADS-B (Automatic Dependent Surveillance-Broadcast) signal coherent receiving method based on an expectation maximization algorithm, which belongs to the technical field of coherent receiving methods, and comprises the following steps of: realizing accurate synchronization of ADS-B signals through energy accumulation and matched filtering, and then estimating and compensating frequency offset and phase offset through the expectation maximization algorithm, and finally, decoding and brute force error correction are carried out. According to the method, information bits behind a leading head are fully considered, the value probability of each bit is analyzed, and actually transmitted data information is taken as a hidden variable, so that the estimation precision of frequency offset and phase offset is greatly improved, the sensitivity index of a satellite-based ADS-B receiver can be effectively improved, the sensitivity of the receiver can reach-102dBm under 90% of correct decoding probability, and the accuracy of the receiver is improved. And meanwhile, the processing delay of the frequency phase offset estimation is less than 50 us, so that the coherent receiving method with limited resources can be used on the small cubesat more easily.

The application discloses an Internet of Things anomaly detection method and system based on quaternion state space diffusion enhancement, and belongs to the technical field of network security and artificial intelligence. The method comprises the following steps: mapping traffic time sequence features into a quaternion tensor to maintain the internal coupling relationship of multi-dimensional features; designing a double-flow encoder, extracting continuous flow features by using a quaternion selective state space model, and modeling discrete protocol features by using a dynamic hypergraph neural network; generating an adversarial pseudo-anomaly sample by using a latent diffusion model to perform self-supervised pre-training, and combining a quaternion cepstrum distance loss to optimize the representation; optimizing the injected learnable prompt vector in the small sample fine-tuning stage, and correcting the class prototype by using a semi-supervised expectation maximization algorithm; calculating sample anomaly scores based on an energy model to realize known attack classification and unknown anomaly determination. The application significantly improves the generalization ability of the model under the condition of small samples and the detection ability of unknown threats.

The present invention discloses a slope-compensated acceleration estimation method based on trajectory data, comprising the following steps: 1. obtaining trajectory data of a vehicle traveling on a slope and a horizontal section; 2. calibrating the vehicle's free flow speed using the trajectory data of the horizontal section; 3. analyzing the slope trajectory data to construct a slope acceleration model; 4. assuming that the expected acceleration of the horizontal section and the compensated acceleration of the slope conform to a normal distribution, according to the acceleration model, obtaining the probability density of the observed acceleration, the posterior probability of the compensated acceleration, and the joint probability distribution of the two; 5. Based on the joint probability distribution, constructing a compensated acceleration estimation model using an expectation-maximization algorithm and performing iterations to obtain the mean and variance of the expected acceleration of the horizontal section and the mean and variance of the compensated acceleration of the slope. The present invention can identify the additional compensated acceleration given to the vehicle by the driver on a slope, thereby better explaining the impact of driving behavior on road capacity.

The invention belongs to the technical field of information and communication, and relates to an active user, time delay and channel joint estimation method for a low-precision receiver. The problem that when a receiver adopts a one-bit analog-to-digital converter, transmission delay and channel state estimation precision are too low due to high quantization errors is solved. Performing correlation peak detection on the received signal and the known leader sequence, and preliminarily estimating active users and transmission time delay; the nonlinear receiving model is equivalent to a linear model based on the Bussgang decomposition principle; an expectation maximization algorithm framework is adopted, related parameters are jointly estimated through inner and outer layer iteration, an inner layer constructs a factor graph model based on a Markov chain to update an equivalent channel and a user state, and an outer layer adopts a local search algorithm to update a transmission delay estimation value. The method is used for a 1-bit low-precision receiver in an asynchronous large-scale machine type communication system, and realizes joint acquisition of active user detection, transmission delay estimation and channel state information.

The application discloses a kind of based on improved particle filtering and convolution network's engraving machine anomaly detection method, and is divided into two stages of offline training and anomaly detection.In the offline training stage, first, collect the data of engraving machine operation and determine the system model structure under normal working condition, then introduce improved particle filtering algorithm to obtain system state variable estimation value, and using expectation maximization algorithm iteratively updates model parameters, obtains the accurate mathematical model of engraving machine system, finally, the residual information of model is used to train one-dimensional residual convolution neural network, realize the two classification of system operation data.In the anomaly detection stage, the noise estimation value sequence is obtained by particle filtering using the mathematical model to the operation data, and finally whether the running data is abnormal is judged by the classifier trained offline.The application can obtain accurate mathematical model of system and mine the correlation of data in time dimension, improve the precision of engraving machine operation data anomaly detection.

The invention discloses a robot trajectory learning generation method and system based on an improved k value selection algorithm, and belongs to the technical field of robot control and trajectory planning. Comprising the steps of obtaining original trajectory data in a robot teaching process, and performing preprocessing; determining an optimal Gaussian kernel number by adopting an improved k value selection algorithm; executing k-means clustering by using the determined optimal k value to obtain an initial mean vector, a covariance matrix and a weight parameter of a Gaussian mixture model; an expectation maximization algorithm is adopted to carry out iterative calculation on the Gaussian mixture model, the iterative calculation obtains the posterior probability of each data point based on Gaussian distribution based on the step E, and the posterior probability is utilized to re-estimate the parameters of the Gaussian mixture model based on the step M; and according to specific task requirements, the expected planning trajectory of the robot is generated through Gaussian mixture regression by using the trained Gaussian mixture model parameters, so that the accuracy of robot trajectory generation is improved.

The invention relates to the technical field of intelligent traffic system and vehicle collaborative perception, in particular to a group vehicle collaborative illegal parking detection method based on vehicle-road multi-element reliability modeling, which comprises the following steps: S1, acquiring sensor data through a plurality of perception vehicles and generating a space-time perception map for the perception vehicles; s2, calculating a road environment reliability score of the detected target vehicle based on the space-time perception map of the perception vehicle; s3, acquiring local detection results of all the sensing vehicles through the server; s4, constructing a multi-vehicle-multi-target detection matrix and a road environment reliability score matrix; s5, constructing a probability generation model; and S6, performing joint reasoning on the probability generation model through an expectation maximization algorithm, and outputting illegal parking state judgment results of all target vehicles. According to the method, the accuracy and recall rate of illegal parking detection and the overall robustness of the system can be improved.

The invention provides an electrochemical energy storage power station planning operation collaborative optimization method considering new energy power generation and load demand uncertainty, and relates to the field of electrochemical energy storage planning and operation. Performing abnormal data processing on the measured value; calculating a relative error value between the standardized source load data and the predicted value, and optimizing mixed parameters of a pre-configured Gaussian mixture model in combination with an adaptive momentum regularization expectation maximization algorithm; constructing an electrochemical energy storage power station configuration optimization model in combination with the source load uncertainty model; and based on an electrochemical energy storage power station configuration optimization model result, combining an electrochemical energy storage power station power balanced distribution strategy of a health degree attenuation effect to realize energy storage power station power distribution. According to the method, the source-load probability distribution is converted into a deterministic power system flexibility requirement by setting the confidence level, a premise is provided for the optimization configuration of a subsequent electrochemical energy storage power station, and the planning reasonability of the energy storage power station is ensured.

A photoelectron spectroscopy method includes: performing photoelectron spectrum detection on a target sample to obtain an initial photoelectron spectrum; selecting spectrum data points according to the initial photoelectron spectrum to obtain a photoelectron spectrum data point sequence; performing a first model parameter update on a preset initial photoelectron spectrum fitting model according to a preset expectation-maximization algorithm and the photoelectron spectrum data point sequence to obtain a first photoelectron spectrum fitting model; acquiring a target quantum effect constraint for the target sample; performing a second model parameter update on the first photoelectron spectrum fitting model according to a preset central field approximation relation and the target quantum effect constraint to obtain a second photoelectron spectrum fitting model; and performing spectrum fitting on the photoelectron spectrum data point sequence according to the second photoelectron spectrum fitting model to obtain a target photoelectron spectrum.

The application discloses a linear variable parameter modeling method for time-delay industrial systems based on exponential optimal smoothing regularization, and belongs to the technical fields of system identification and industrial automation. The application aims at the problems that the modeling method adopted by the existing industrial systems estimates model parameters and time-delay parameters in steps, accumulates errors and causes low model precision. The application comprises the following steps: a local linear finite impulse response time-delay model is established, and a global model of the time-delay industrial system is obtained; a model identity hidden variable is introduced in a probability framework, a probability density function of global output of the global model is obtained based on local output distribution characteristics; a smoothing matrix is constructed based on an exponential optimal smoothing regularization method, and a prior distribution of local model parameters is obtained; an observation data set and a missing data set are established, an iterative updating formula of global model parameters is obtained based on a generalized expectation maximization algorithm, and global model parameter estimation values are obtained after the algorithm converges, so that the modeling of the global model is realized. The application is used for linear variable parameter modeling of time-delay industrial systems.

The invention relates to the technical field of mineral processing engineering, and discloses an ore grinding granularity prediction method combining missing value completion and multi-model collaboration. According to the method, a missing feature complementation module is constructed based on a Gaussian mixture model and an expectation maximization algorithm, and a linear distribution regression branch and a gradient lifting branch are constructed based on a linear regression model and a gradient lifting model respectively, so that an ore grinding granularity prediction model is formed. The method comprises the following steps: acquiring and preprocessing multi-source ore grinding operation data to obtain input data containing missing features, and carrying out probability modeling and completion on the missing features by utilizing a completion module; and the linear distribution regression branch and the gradient lifting branch perform modeling on the completion features respectively, output corresponding ore grinding particle size probability distribution prediction results, and fuse multi-branch prediction results to obtain final ore grinding particle size prediction distribution. According to the method, the robustness, precision and prediction stability of ore grinding particle size prediction under complex working conditions are effectively improved.